很高兴在2012年1月1日接收了第2篇journal论文,望能够与做相关研究（network science, machine learning and bioinformatics）的同志交流与学习了。

 

Abstract： Detecting protein complexes from protein interaction networks is one major task in the post-genome era. Previous developed computational algorithms identifying complexes mainly focus on graph-partition or dense region-finding.  Most of these traditional algorithms cannot discover overlapping complexes which really exist in the protein-protein interaction (PPI) networks. Even if some density-based methods have been developed to identify overlapping complexes, they are not able to discover complexes that include peripheral proteins. In this study, motivated by recent successful application of generative network model to describe the generation process of  PPI networks and to detect communities from social networks, we develop a regularized sparse generative network model (RSGNM), by  adding another process that generates propensities using exponential distribution and incorporating Laplacian regularizer into an existing generative network model, for protein complexes identification. By assuming that the propensities are generated using exponential distribution,  the estimators of propensities will be sparse, which not only has good  biological interpretation but also helps to control the overlapping rate among detected complexes. And the Laplacian regularizer will lead to the estimators of propensities more  smooth on interaction networks. Experimental results on three  yeast PPI networks show that RSGNM outperforms six previous competing algorithms in terms of  the quality of detected complexes. In addition, RSGNM is able to detect overlapping complexes and complexes including peripheral proteins simultaneously. These results give new insights about the importance of generative network models in protein complexes identification. 

 

X. F. Zhang, D. Q. Dai and X. X. Li, Protein complexes discovery based on protein-protein interaction data via a regularized sparse generative network model,  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012 accepted